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Physiological Genetics Reformed: Bridging the Genome-to-Phenome Gap by Coherent Chemical Fingerprints – the Global Coordinator
Abstract
Forward-focused molecular genetics is successfully framing DNA diversity and mapping primary gene functions. However, abandoning the classic Linnaean fingerprint link between the phenome and genome by suppressing gene interaction (pleiotropy), has resulted in a genome-to-phenome gap and poor utilization of molecular data. We demonstrate how to bridge this gap by using an example of a barley mutant seed model, where pleiotropy is observed as covarying global molecular patterns that define each endosperm. Global coherence was discovered as a covariate coordinator within and between local genotype specific fingerprints. This implies that any of these fingerprints can select its recombinant global phenotype variant, including composition. Introducing the law of coherence, and the movement of gene complexes by chemical fingerprint traits as selectors, introduces a revolution in understanding physiological molecular genetics and plant-breeding.
... A significantly negative correlation among concentrations of metabolites in the studied common buckwheat material shows that there are different mechanisms of genetic influences on the concentration of phenolic substances in common buckwheat flowers, leaves, and grains. To reveal, how the mechanisms perform, it is necessary to ask the simplified general exploratory question by changing one genetic factor or one environment at a time [50]. These differences should be taken into account when breeding buckwheat for a high concentration of selected phenolic substances and in the selection of cultivars for diverse nutritional and pharmaceutical purposes. ...
Common buckwheat (Fagopyrum esculentum Moench) is a valuable source of proteins, B vitamins, manganese, tryptophan, phytochemicals with an antioxidant effect, and the natural flavonoid rutin. Due to its composition, buckwheat supports the human immune system, regulates blood cholesterol, and is suitable for patients with diabetes or celiac disease. The study aimed to compare the allocation of selected phenolic acids (neochlorogenic acid, chlorogenic acid, trans-caffeic acid, trans-p-coumaric acid, trans-sinapic acid, trans-ferulic acid) and flavonoids (rutin, vitexin, quercetin, kaempferol) in the leaves, flowers, and grain of buckwheat cultivars of different origin. The content of individual phenolics was determined by the HPLC-DAD method. The results confirmed the determining role of cultivar on the relative content of chlorogenic acid, trans-caffeic acid, trans-sinapic acid, vitexin, and kaempferol in buckwheat plants. A significantly negative correlation among concentrations of phenolic acids in different common buckwheat plant parts shows that there are different mechanisms of genetic influences on the concentration of phenolic substances in common buckwheat flowers, leaves, and grain. These differences should be taken into account when breeding buckwheat for a high concentration of selected phenolic substances.
We recently completed a Hort Innovation funded project, AV19006, in which non-destructive methods to monitor tree carbohydrate status in avocado were reviewed. Based on seasonal fluctuations in carbohydrate reserves, it was previously proposed that tree carbohydrate status is a major factor that influences flowering, fruit set and fruit retention and, therefore, is an important determinant for predicting and maximising yieldl. The project began in June 2020 and was completed in May 2021. The first objective of the work was to perform a 'desktop' analysis to review non-destructive approaches that might be developed for estimating tree carbohydrate levels in avqcado. The desktop review identified near-infrared (NIR) reflectance spectroscopy as the technology with the most potential for in-field commercial application, but also considered the possibility of applying a modelling approach, requiring no in-field measurements, specifically photothermal quotient (PTQ). The subsequent objective of the project was to undertake a proof-of-concept trial of these two approaches, where they were evaluated in order to assess their effectiveness. Based on the results of this project future R&D directions are discussed.
/Users/larsmunck/Desktop/Final copy TIPS 16.2.21. TRPLSC_2083_15 Feb21.pdf
Hyperspectral imaging has proved its potential for evaluating complex plant-pathogen interactions. However, a closer link of the spectral signatures and genotypic characteristics remains elusive. Here, we show relation between gene expression profiles and specific wavebands from reflectance during three barley—powdery mildew interactions. Significant synergistic effects between the hyperspectral signal and the corresponding gene activities has been shown using the linear discriminant analysis (LDA). Combining the data sets of hyperspectral signatures and gene expression profiles allowed a more precise differentiation of the three investigated barley-Bgh interactions independent from the time after inoculation. This shows significant synergistic effects between the hyperspectral signal and the corresponding gene activities. To analyze this coherency between spectral reflectance and seven different gene expression profiles, relevant wavelength bands and reflectance intensities for each gene were computed using the Relief algorithm. Instancing, xylanase activity was indicated by relevant wavelengths around 710 nm, which are characterized by leaf and cell structures. HvRuBisCO activity underlines relevant wavebands in the green and red range, elucidating the coherency of RuBisCO to the photosynthesis apparatus and in the NIR range due to the influence of RuBisCO on barley leaf cell development. These findings provide the first insights to links between gene expression and spectral reflectance that can be used for an efficient non-invasive phenotyping of plant resistance and enables new insights into plant-pathogen interactions.
The development of crop varieties tolerant to growth temperature fluctuations and improved nutritional value is crucial due to climate change and global population growth. This study investigated the metabolite patterns of developing barley seed as a function of genotype and growth temperature for ideal vegetable protein production and for augmented β-glucan production. Seeds from three barley lines (Bomi, lys3.a and lys5.f) were sampled eight times during grain filling and analysed for metabolites using gas chromatography-mass spectrometry (GC-MS). The lys3.a mutation disrupts a regulator gene, causing an increase in proteins rich in the essential amino acid lysine, while lys5.f carries a mutation in an ADP-glucose transporter gene leading to a significant increase in production of mixed-linkage β-glucan at the expense of α-glucan. Unique metabolic patterns associated with the tricarboxylic acid cycle, shikimate-phenylpropanoid pathway, mevalonate, lipid and carbohydrate metabolism were observed for the barley mutants, whereas growth temperature primarily affected shikimate-phenylpropanoid and lipid metabolism. The study applied recently developed GC-MS metabolomics methods and demonstrated their successful application to link genetic and environmental factors with the seed phenotype of unique and agro-economically important barley models for optimal vegetable protein and dietary fibre production.
Near infrared technology, now widespread in quality control, makes it possible to obtain a total multivariate physical chemical fingerprint of the barley endosperm with high precision. Whole spectroscopic fingerprints of the physics and chemistry of barley seeds can be interpreted by multivariate analysis (chemometrics), by Principal Component Analysis (PCA) for classification and Partial Least Squares Regression (PLSR) for correlation. PCA classification of Near Infrared Reflectance (NIR) spectra can differentiate between mutants and alleles in the lys3 and lys5 loci. PCA on NIR can also be used as a routine in barley breeding to select for a multi-gene quality complex in barley as a whole e.g. increasing starch and reducing fibre content. This is done directly from the PCA classification plot by “data breeding” selecting the recombinants which are approaching the position of the normal high starch controls on the plot. Based on classification of NIR spectra, two alleles in the lys5 locus were characterised as a new class of (1→3,1→4)--glucan compensating starch mutants indicating a metabolic connection between starch and -glucan.
Photosynthetic antenna complexes harvest sunlight and efficiently transport energy to the reaction center where charge separation powers biochemical energy storage. The discovery of existence of long lived quantum coherence during energy transfer has sparked the discussion on the role of quantum coherence on the energy transfer efficiency. Early works assigned observed coherences to electronic states, and theoretical studies showed that electronic coherences could affect energy transfer efficiency-by either enhancing or suppressing transfer. However, the nature of coherences has been fiercely debated as coherences only report the energy gap between the states that generate coherence signals. Recent works have suggested that either the coherences observed in photosynthetic antenna complexes arise from vibrational wave packets on the ground state or, alternatively, coherences arise from mixed electronic and vibrational states. Understanding origin of coherences is important for designing molecules for efficient light harvesting. Here, we give a direct experimental observation from a mutant of LH2, which does not have B800 chromophores, to distinguish between electronic, vibrational, and vibronic coherence. We also present a minimal theoretical model to characterize the coherences both in the two limiting cases of purely vibrational and purely electronic coherence as well as in the intermediate, vibronic regime.
We demonstrate quantum information can be transferred between two distant participants without any physical particles traveling between them. The key procedure of the counterfactual scheme is to entangle two nonlocal qubits with each other without interaction, so the scheme can also be used to generate nonlocal entanglement counterfactually. We here illustrate the scheme by using flying photon qubits and Rydberg atom qubits assisted by a mesoscopic atomic ensemble. Unlike the typical teleportation, the present scheme can transport an unknown qubit in a nondeterministic manner without prior entanglement sharing or classical communication between the two distant participants.
Must higher level biological processes always be derivable from lower level data and mechanisms, as assumed by the idea that an organism is completely defined by its genome? Or are higher level properties necessarily also causes of lower level behaviour, involving actions and interactions both ways? This article uses modelling of the heart, and its experimental basis, to show that downward causation is necessary and that this form of causation can be represented as the influences of initial and boundary conditions on the solutions of the differential equations used to represent the lower level processes. These insights are then generalized. A priori, there is no privileged level of causation. The relations between this form of 'biological relativity' and forms of relativity in physics are discussed. Biological relativity can be seen as an extension of the relativity principle by avoiding the assumption that there is a privileged scale at which biological functions are determined.
High throughput gene expression technologies are a popular choice for researchers seeking molecular or systems-level explanations of biological phenomena. Nevertheless, there has been a groundswell of opinion that these approaches have not lived up to the hype because the interpretation of the data has lagged behind its generation. In our view a major problem has been an over-reliance on isolated lists of differentially expressed (DE) genes which - by simply comparing genes to themselves - have the pitfall of taking molecular information out of context. Numerous scientists have emphasised the need for better context. This can be achieved through holistic measurements of differential connectivity in addition to, or in replacement, of DE. However, many scientists continue to use isolated lists of DE genes as the major source of input data for common readily available analytical tools. Focussing this opinion article on our own research in skeletal muscle, we outline our resolutions to these problems - particularly a universally powerful way of quantifying differential connectivity. With a well designed experiment, it is now possible to use gene expression to identify causal mutations and the other major effector molecules with whom they cooperate, irrespective of whether they themselves are DE. We explain why, for various reasons, no other currently available experimental techniques or quantitative analyses are capable of reaching these conclusions.
This investigation is principally aimed at exemplifying the developmental sources (italicized below) that are the requisites to obtain an adequate nutritional role for cereals in feeds and foods.
Barley for feed purposes is given central attention and is compared with wheat, rye, ryewheat and oats. The importance of basic research in plant and animal sciences is briefly reviewed, and also complemented with experimental results. Development of reliable, rapid and inexpensive screening methods is stressed and exemplified by a feeding test with mice and a dye-binding (DBC) method for estimating lysine. Screening analyses for quality characters in cereals are used in inventories studying cereals, feed supplements, and complete commercial feeds. The information obtained is tentatively evaluated and is used for promulgating a provisional development programme for Swedish conditions. The research that has been carried out for the realization of the programme is discussed with attention focused particularly on the development of high-lysine barleys and a high-temperature drying process for cereals for livestock feeds.
A high-protein, high-lysine barley line (Hiproly) was recovered from the World Barley Collection employing the DBC method. A recessive gene (lys) was found in Hiproly that increases lysine and other essential amino acids in the endosperm independently of protein content. Genetical, plant-physiological, biochemical, ultrastructural and animal nutritional aspects of high-lysine genes in barley and in maize are extensively discussed along with concomitant analyses of the lys gene.
Problems involving grain deterioration and its prevention in Scandinavian cereal production are considered. In addition, the effect of heat on the availability of lysine in cereals during storage, drying and baking is experimentally studied. On the basis of the observations and results, a full-scale, rapid, high-temperature drying method has been developed that is adopted in agriculture in Sweden.
The results are summarized and confronted with the operational concept of nutritional value. The needs for retrieval of information from research and from the applied sphere of cereal utilization — all for optimal exploitation by plant breeders — are discussed for conditions prevalent in Sweden, on the one hand, and in the developing countries, on the other. Research data from basic sciences as well as information from regional inventories on technological, ecological and social conditions have to be co-ordinated in a synthesis in science specific for each country. New solutions must be transferred to the individual and group level of people who can use them as selection criteria favouring survival and quality in economy and life.
There is a need for an improved biological and theoretical interpretation of Near Infra-Red Spectral (NIRS) fingerprints from tissues that could contribute with holistic overview to fine-grained detail modelled in Systems Biology. The concept of gene expression in self-organised networks was experimentally tested in a barley endosperm model with molecularly defined and undefined mutants. Surprisingly reproducible gene-specific NIRS fingerprints were observed directly in log1/R MSC pre-treated spectra that could not be accurately represented by destructive mathematical models. A mutant spectrum in an isogenic background represents the physiochemical expression of the gene in the whole network (tissue). The necessary holistic overview that is needed experimentally to introduce Ilya Prigogine's theory on self-organisation in Systems Biology was supplied by defining the spectral phenome. Interval spectral information on genotypes and environment was classified by interval Extended Canonical Variates Analysis (iECVA). Genetic changes in spectra were interpreted by interval Partial Least Squares Regression (iPLSR) correlations to chemical variables. A new pathway regulation was detected. The finely grained ‘bottom up’ modelling of molecular and chemical data from pathways requires a coarsely grained exploratory ‘top down’ overview by NIRS to account for the outcome of self-organisation. The amplification of expression from a gene to the phenome (pleiotropy) can now for the first time be quantified as a whole reproducible phenomenological pattern by NIRS and compared to other gene spectra. It explains published findings that transformed respectively mutated genes in genetically modified organisms (GMOs) and cancer patients can be detected unsupervised from tissues by spectroscopy, chemometrics and data inspection. Copyright © 2007 John Wiley & Sons, Ltd.
Evaluating gene effects on proteomes and the resulting indirect pleiotropic effects through the cell machinery on the chemical phenotype constitutes a formidable challenge to the analytical chemist. This paper demonstrates that near-infrared (NIR) spectroscopy and chemometrics on the level of the barley seed phenotype is able to differentiate between genetic and environmental effects in a PCA model involving normal barley lines and the gene regulator lys3a in different genetic backgrounds. The gene drastically changes the proteome quantitatively and qualitatively, as displayed in two-dimensional electrophoresis, resulting in a radically changed amino acid and chemical composition. A synergy interval partial least squares regression model (si-PLSR) is tested to select combinations of spectral segments which have a high correlation to defined chemical components indicative of the lys3a gene, such as direct effects of the changed proteome, for example, the amide content, or indirect effects due to changes in carbohydrate and fat composition. It is concluded that the redundancy of biological information on the DNA sequence level is also represented at the phenotypic level in the dataset read by the NIR spectroscopic sensor from the chemical physical fingerprint. The PLS algorithm chooses spectral intervals which combine both direct and indirect proteome effects. This explains the robustness of NIR spectral predictions by PLSR for a wide range of chemical components. The new option of using spectroscopy, analytical chemistry and chemometrics in modeling the genetically based covariance of physical/chemical fingerprints of the intact phenotype in plant breeding and biotechnology is discussed.
Principal component analysis of a data matrix extracts the dominant patterns in the matrix in terms of a complementary set of score and loading plots. It is the responsibility of the data analyst to formulate the scientific issue at hand in terms of PC projections, PLS regressions, etc. Ask yourself, or the investigator, why the data matrix was collected, and for what purpose the experiments and measurements were made. Specify before the analysis what kinds of patterns you would expect and what you would find exciting.The results of the analysis depend on the scaling of the matrix, which therefore must be specified. Variance scaling, where each variable is scaled to unit variance, can be recommended for general use, provided that almost constant variables are left unscaled. Combining different types of variables warrants blockscaling.In the initial analysis, look for outliers and strong groupings in the plots, indicating that the data matrix perhaps should be “polished” or whether disjoint modeling is the proper course.For plotting purposes, two or three principal components are usually sufficient, but for modeling purposes the number of significant components should be properly determined, e.g. by cross-validation.Use the resulting principal components to guide your continued investigation or chemical experimentation, not as an end in itself.
Expansion of the human population demands a significant increase in cereal production. The main component of cereal grains is endosperm, a body of starchy endosperm (SE) cells surrounded by aleurone (AL) cells with transfer cells (TC) at the base and embryo surrounding (ESR) cells adjacent to the embryo. The data reviewed here emphasize the modular nature of endosperm by first suggesting that sucrose promotes development of the fertilized triploid endosperm cell. Next, that the basal syncytial endosperm responds to glucose by turning on TC development. The default endosperm cell fate is SE and ESR differentiation is likely activated by signaling from the embryo. Cells on the exterior surface of the endosperm are specified as AL cells.
Despite the huge place molecular biology has acquired in biological research, it remains difficult to provide a definition of it. Is molecular biology a scientific discipline, or a new vision of organisms? When did it emerge? Is molecular biology still alive, or has this discipline died, and been replaced by new disciplines such as systems and synthetic biology? Were molecular biologists too reductionist?
Three successive steps can be distinguished in the history of molecular biology: the 1930s, with the development of new technologies aimed at describing the structure of macromolecules, and an effort to ‘naturalise life’; a relatively short period (1940–1965) in which the main results were obtained; and the huge accumulation of molecular data that has modified biology since this time. Despite the fact that molecular explanations have in part reached their limits, I consider that molecular biology has succeeded in ‘naturalising life’.
Key Concepts
Molecular biology is not a new discipline, but a new level of explanation.
Biologists are still working within the framework of the ‘molecular paradigm’ built more than 50 years ago.
Structural biology is an essential, but not a highly visible part of molecular biology.
Personalised medicine is a consequence of the progress made in DNA sequencing.
It is our custom in NIR news to invite the THA winner to contribute a summary of their award lecture in an issue soon after the ceremony. Unfortunately the 2015 award article was overlooked but we are happy to have prevailed upon the winner, Søren Engelsen, to allow us to maintain this custom by writing something for us. In an exciting summary article, Søren initially recaps on some practical aspects of NIR spectroscopy and reminds us of why it is such a useful technique. In a look to the future, however, he holds out some tantalising glimpses of what developments might be just around the corner and how these might produce sea changes in our thinking and use of the method — how about instruments which require no calibration transfer operations? Read and enjoy! Oh and the lecture summary by the 2016 winner will appear in 2016!! Ed.
Plant photosynthesis and grain production is the basis for a sustainable environment supporting human life that should not be compromised for the generations to come. It is expected that world grain production has to increase by about 50% up to the year 2050 to support a population of up to 11 billion people. The key question is how and if such a change can be introduced for a sustainable intensification with a minimum of damage to environment and human health still keeping the production potential intact? The combination of new efficient analytical methods in molecular biology (genomics, proteomics) with new megavariate instrumental phenotypic analyses (phenomics, metabolomics) evaluated by pattern recognition computer tools (chemometrics) creates a revolution in an integrated science of plant breeding, grain production and environment. The new concept makes GMO (genetic modified organisms) breeding sustainable and uncontroversial when utilizing certified gene resources and vectors within the plant kingdom. In order to properly exploit the new biologically adapted grain varieties obtained by integrated breeding, human wearing of the planet must be diminished by changing the related infrastructure. In implementing these changes one has to create economic incentives to channel the present rising wealth of the world economy into investments in connecting rural and urban living by the concept of the bio-refinery that could conserve energy and recycle organic waste and water. Such an integrated improvement in infrastructure is the basis for securing clean water and energy, fertile soils with vital microorganisms, biodiversity, green cities, food supply, and social rest.
Reflecting the major advances that have been made in the field over the past decade, this book provides an overview of current models of biological systems. The focus is on simple quantitative models, highlighting their role in enhancing our understanding of the strategies of gene regulation and dynamics of information transfer along signalling pathways, as well as in unravelling the interplay between function and evolution. The chapters are self-contained, each describing key methods for studying the quantitative aspects of life through the use of physical models. They focus, in particular, on connecting the dynamics of proteins and DNA with strategic decisions on the larger scale of a living cell, using E. coli and phage lambda as key examples. Encompassing fields such as quantitative molecular biology, systems biology and biophysics, this book will be a valuable tool for students from both biological and physical science backgrounds.
Since prehistoric times man has had the capability, stimulated by the diversity of nature, to select and utilize various items from his environment. At first man influenced only his immediate surroundings to improve his life and survival, but later he seriously intervened with the total global environment by exploiting the fossil energy resources. In this path, agriculture has constituted the basis of human development, securing a food store which enabled man to plan and to specialize in fields like administration, military defense, teaching and technology as well as in science, philosophy and arts.
In the light of the original work by Darwin this paper first discusses the modern plant and animal breeder as a selector driving his selection cycles, as a heuristic model of the basic human activity of experimental behaviour. This behaviour is also applicable for consumers in supermarkets and is now studied in social science as value theory. The development of human civilization based on agriculture is further looked upon as a gigantic breeding process involving not only plants and animals but also machinery and human concepts, such as law and money.
Our environmental dilemma is seen as caused by the unintentional side-effect of the selection force of the now more than 5 billion human beings selecting their means for survival and enjoying life on our common planet. The serious limitations of the human mind, including the mind of scientists, to overview and predict complex systems are elucidated by the model.
New strategies based on data systems and interdisciplinary thinking may bring us further in planning our future by learning from our mistakes. This will, however, not be effective if we do not realize in time the true consequences for man and environment of the concepts inherent in our culture, such as the monetary concept, many of which originate from the implementation of agriculture.
The formidable success of classical science and technology through the last 300 years, in exploiting fossil resources in industrializing agriculture, has brought a wide range of inventions and insights, in the form of marketable products supporting, solving and explaining many detailed functions in our daily life which are most attractive and impressive for the individuals, stimulating consumption beyond basic needs. At present, we are concerned about problems with regard to overpopulation and the environment related to the consumption of all kinds of resources which we are unable to tackle systematically because we have not yet developed a realistic primary hypothesis on ‘the metabolism’ of our world.
Barley is one of the cereals in which the possibilities for improving the nutritional value of the seed protein have been studied extensively. Detailed knowledge of barley proteins and their genetics is available (for a survey see, e.g. Miflin and Shewry [43], Thomson and Doll [66], and Tallberg [65]). Increases in the content of lysine and other essential amino acids of the protein have been achieved, but unfortunately, the changes in protein composition interfere with the carbohydrate accumulation in the seed. It has not yet been possible to produce a barley variety with improved protein quality and normal grain yield.
Near-infrared (NIR) spectroscopy technology is an extremely successful tool in the cereal industry, allowing fast screening methods for the prediction of specific analytes. NIR spectroscopy analyses make quality control instant and move it from the laboratory to the production line. This chapter presents two NIR spectroscopy instruments, one for NIR reflectance on flour and one for near-infrared transmittance (NIT) transmission on whole seeds. It is essential in NIR spectroscopy to explore the sequential information on chemical bonds and components given in local bands in the spectra in order to ensure the reliable results. Interval partial least squares regression (iPLS) at different intervals (5-35 nm) is employed because of a graphic interface that is especially useful in visualizing the "hot spots" in collection of spectra for the highest correlation and lowest error. NIT and NIR reflectance spectrometers are widely used in the industry to measure moisture, protein, starch, and fiber content in whole maize, and in maize products. NIR spectroscopy prediction models are available for the estimation of extractable starch for the wet milling industry and for predicting true density, seed breakage susceptibility, and kernel density in maize. A modified least squares regression analysis of NIR spectroscopy has been employed to check the authenticity of Korean grown rice versus imported rice for short and medium long grains.
Fifty years ago Francis Crick and James D. Watson proposed the double helix model for the DNA molecule. They believed they had, as Crick put it, discovered the "secret of life," and many agreed. But in the intervening years, science has marched--sometimes leaped--forward, and now the question "What is life?" must be posed once again. In this accessible and fascinating book, Michel Morange draws on recent advances in molecular genetics, evolutionary biology, astrobiology, and other disciplines to find today's answers to the question of life. He begins by discussing the various answers that have been formulated in the past, setting contemporary definitions of life within a rich philosophical and scientific tradition that reaches back to ancient Greece. Then, with impeccable logic and a wealth of appropriate detail, Morange proceeds to lay out the fundamental characteristics that define life. The road to an understanding of life remains incompletely charted, he concludes, but the nature of its final destination is no longer an enigma.
DOI:https://doi.org/10.1103/RevModPhys.85.1103
I have tried to give a brief account of some of the developments that have taken place in the area of quantum state manipulation of small numbers of trapped atomic ions. With apologies, I have omitted several aspects of this subject and for the topics discussed here, I primarily used examples from the NIST, Boulder group. Much of the other work has been discussed in various comprehensive articles and reviews; see for example (Blatt and Roos 2012; Blatt and Wineland 2008; Cirac et al. 1996; Duan and Monroe, 2008, 2010; Haffner et al., 2008; Kielpinski, 2008; Korenblit et al., 2012; Lee et al., 2005; Leibfried et al. 2003a; Monroe and Lukin 2008; Monroe et al. 2012; Schneider et al. 2012; Sasura and Buzek 2002; Wineland et al. 1998). Reviews on advanced clocks including those based on ions are contained in Gill 2005, 2011; Maleki 2008; and Margolis 2009. See also Madej et al. 2012 and references therein.
Biometrics has done damage with levels of R or p or Student's t. The damage widened with R. A. Fisher's victory in the 1920s and 1930s in devising mechanical methods of "testing," against methods of common sense and scientific impact, "oomph." The scale along which one would measure oomph is particularly clear in bio-medical sciences: life or death. Cardiovascular epidemiology, to take one example, combines with gusto the "fallacy of the transposed conditional" and what we call the "sizeless stare" of statistical significance. Some medical editors have battled against the 5% philosophy, as did for example Kenneth Rothman, the founder of Epidemiology. And decades ago a sensible few in education, ecology, and sociology initiated a "significance test controversy." But grantors, journal referees, and tenure committees in the statistical sciences had faith that probability spaces can substitute for scientific judgment. A finding of p < . 05 is deemed to be "better" for variable X than p < .11 for variable Y. It is not. It depends on the oomph of X and Y—the effect size, size judged in the light of how much it matters for scientific or clinical purposes. In 1995 a Cancer Trialists' Collaborative Group, for example, came to a rare consensus on effect size: ten different studies had agreed that a certain drug for treating prostate cancer can increase patient survival by 12%. An eleventh study published in the New England Journal in 1998 dismissed the drug. The dismissal was based on a t test, not on what William Gosset (the "Student" of Student's t) had called, against R. A. Fisher's machinery, "real" error.
This paper is concerned with the quantitative analysis of multicomponent mixtures by diffuse reflectance spectroscopy. Near-infrared reflectance (NIRR) measurements are related to chemical composition but in a nonlinear way, and light scatter distorts the data. Various response linearizations of reflectance (R) are compared ( R with Saunderson correction for internal reflectance, log 1/ R, and Kubelka-Munk transformations and its inverse). A multi-wavelength concept for optical correction (Multiplicative Scatter Correction, MSC) is proposed for separating the chemical light absorption from the physical light scatter. Partial Least Squares (PLS) regression is used as the multivariate linear calibration method for predicting fat in meat from linearized and scatter-corrected NIRR data over a broad concentration range.
All the response linearization methods improved fat prediction when used with the MSC; corrected log 1/ R and inverse Kubelka-Munk transformations yielded the best results. The MSC provided simpler calibration models with good correspondence to the expected physical model of meat. The scatter coefficients obtained from the MSC correlated with fat content, indicating that fat affects the NIRR of meat with an additive absorption component and a multiplicative scatter component.
There is a story about two friends, who were classmates in high school, talking about their jobs. One of them became a statistician and was working on population trends. He showed a reprint to his former classmate. The reprint started, as usual, with the Gaussian distribution and the statistician explained to his former classmate the meaning of the symbols for the actual population, for the average population, and so on. His classmate was a bit incredulous and was not quite sure whether the statistician was pulling his leg. “How can you know that?” was his query. “And what is this symbol here?” “Oh,” said the statistician, “this is π.” “What is that?” “The ratio of the circumference of the circle to its diameter.” “Well, now you are pushing your joke too far,” said the classmate, “surely the population has nothing to do with the circumference of the circle.”
When predicting the chemical composition of food samples from near-infrared spectroscopy using partial least squares regression, deep knowledge of the origin of the information is not present. We are aiming at opening a Pandora's box of how the prediction of protein proceeds in a unique set of chemically diverse barley mutant samples. An external validation of the sources of co-variation in nature that are exploited by chemometric models would give a framework for manipulating the deciding information to make expensive calibration more economical. The barley samples were supplemented by two designed data sets: one mirroring the coarse composition of the barley samples by mixing six main chemical components and one set where the biological covariance between the six chemical components had been reduced.
An extension of chemometric theory was experimentally explored to explain the physiochemical basis of the very high efficiency of soft modelling of data from nature. Soft modelling in self-organisation was interpreted by studying the unique chemical patterns of mutants in an isogenic barley model on endosperm development. Extremely reproducible, differential Near Infrared (NIR) spectral patterns specifically overviewed the effect on cell composition of each mutant cause. Extended Canonical Variates Analysis (ECVA) classified spectra in wild type, starch and protein mutants. The spectra were interpreted by chemometric data analysis and by pattern inspection to morphological, genetic, molecular and chemical information. Deterministic chemical reactions were defined in the glucan pathway. A drastic mutation in a gene controlling the starch/ß-glucan composition changed water activity that introduced a diffusive, stochastic effect on the catalysis of all active enzymes. ‘Decision making’ in self-organisation is autonomous and performed by the soft modelling of the chemical deterministic and stochastic reactions in the endosperm cell as a whole. Uncertainty in the analysis of endosperm emergence was experimentally delimited as the ‘indeterminacy’ in local molecular path modelling ‘bottom up’ and the ‘irreducibility’ of the phenomenological NIR spectra ‘top down’. The experiment confirmed Ilya Prigogine's interpretation of self-organisation by his dynamic computer model programmed with a self-modeled non-local extension of quantum mechanics (QM). The significance of self- organisation explained by Prigogine here interpreted as physiochemical soft modelling introduces a paradigm shift in macroscopic science that forwards a major argument for soft mathematical modelling and chemometrics to obtain full scientific legitimacy. Copyright © 2010 John Wiley & Sons, Ltd.
Introduction and Scope The Need for a Connection between the Micro and Macro Aspects of the World Introducing an Experimental Model of Self - Organization Based on Near Infrared Spectroscopy (NIRS) of the Barley Endosperm Mutant Model Interpreted by Chemometrics Chemical and Genetic Interpretation of the NIR Spectral Overviews Explains the Limits in Mathematical Evaluation of Data from Emergent Systems Crossing the Frontier between the Micro and Macro Aspects of Emergence Surveillance of Self - Organization of the Biosphere by Spectroscopy and Image Analysis as a Basis to Predict Climate and Growth Conditions for Plants Strengthening the Symbiosis between the Cereal Plant and the Human Society Conclusion: Man as Selector - A Darwinian Boomerang Striking through Natural Selection Acknowledgments References
Chemometric models including data pre-treatment and inspection software are able to evaluate the coarsely grained “top down” observational data from biological tissues through spectroscopic sensors in a dialogue with fine-grained analytical “bottom up” data in a sequential exploratory selection strategy. It facilitates interpretation and development of new theoretical concepts. This is demonstrated by a data set of 11 barley mutant endosperm genes and crosses focusing on the extremely high mutant gene discrimination capability by Near Infrared Reflection Spectroscopy (NIRS). It represents patterns of intact chemical bonds from self-organised endosperm tissues, which are validated to prior spectroscopic, chemical and genetic knowledge. Principal Component Analysis (PCA) of spectra could classify different endosperm mutant genes and changed gene backgrounds. A new carbohydrate pathway regulation from starch to β-glucan was identified. Specific mutant genes were defined visually by directly inspecting and validating spectral patterns from genetically defined barleys. Genetic concepts such as “the phenome” and “pleiotropy” were given new definitions, phenomenologically expressed as log1/R MSC (Multiple Scatter Corrected) NIRS fingerprints comparing mutants in an iso-genic background. Chemometric models are efficient in over-viewing genetic and environmental spectral differences but are not able to reproduce and predict in detail the finely tuned spectra from the “natural computer” of the self-organising tissue. Thus it is always necessary to return to spectral data for a final visual evaluation. In statistics and chemometrics mathematical formalism in data modelling should be integrated with the chemical and biological meaning of data-patterns. Ideas for problem solution may be combined from both sides.
Near infrared spectroscopic (NIR; 1100–2500 nm), chemical and genetic data were combined to study the pleiotropic secondary effects of mutant genes on milled samples in a barley seed model. NIR and chemical data were both effective in classifying gene and gene combinations by Principal Component Analysis (PCA). Risø mutants R-13, R-29 high (1→3, 1→4)-β-glucan, low starch and R-1508 (high lysine, reduced starch), near isogeneic controls and normal lines and recombinants were studied. Based on proteome analysis results, six anti-microbial proteins were followed during endosperm development revealing pleiotropic gene effects in expression timing that supporting the gene classification. To verify that NIR spectroscopy data represents a physio–chemical fingerprint of the barley seed, physical and chemical spectral components were partially separated by Multiple Scatter Correction and their genetic classification ability verified. Wavelength bands with known water binding and (1→3, 1→4)-β-glucan assignments were successfully predicted by partial least squares regression giving insight into how NIR-data works in classification. Highly reproducible gene-specific, covariate, pleiotropic classification patterns from NIR and chemical data were demonstrated in PCAs and by visual inspection of NIR spectra. Thus PCA classification of NIR-data gives the classical genetic concept, ‘pleiotropy’, a new operational definition as a fingerprint from a spectroscopic representation of the phenome carrying genetic, physical and chemical information. It is concluded that barley seed phenotyping by NIR and chemometrics is a new, reliable tool for characterising the pleiotropic effects of mutant gene combinations and other genotypes in selecting barley for quality in plant breeding.
Near Infrared Reflectance spectroscopy was tested as a screening method to characterise high lysine mutants from a barley collection by classification through Principal Component Analysis (PCA). Mean spectra of the samples within each cluster identified gene-specific patterns in the 2270–2360 nm region. The characteristic spectral signatures representing the lys5 locus (Risø mutants 13 and 29) were found to be associated with large changes in percentage of starch and (1→3,1→4)-β-glucan. These alleles compensated for a low level of starch (down to 30%) by a high level of (1→3,1→4)-β-glucan (up to 15–20%), thus, maintaining a constant production of polysaccharides at 50–55%, within the range of normal barley.The spectral tool was tested by an independent data set with six mutants with unknown polysaccharide composition. Spectral data from four of these were classified within the high (1→3,1→4)-β-glucan BG lys5 cluster in a PCA. Their high (1→3,1→4)-β-glucan and low starch content was verified. It is concluded that genetic diversity such as from gene regulated polysaccharide and storage protein pathways in the endosperm tissue can be discovered directly from the phenotype by chemometric classification of a spectral library, representing the digitised phenome from a barley gene bank.
The "metabolome" comprises the entire complement of small molecules in a plant or any other organism. It represents the ultimate phenotype of cells, deduced from the perturbation of gene expression and the modulation of protein function, as well as environmental cues. Extensive advances over the past decade, regarding the high-throughput (HTP) nature of "omics" research, have given birth to the expectation that a type of "systems level" overview may soon be possible. Having such a global overview of the molecular organization of a plant in the context of a particular set of genetic or environmental conditions, be it at cell, organ, or whole plant level, would clearly be very powerful. Currently, we are far from achieving this goal; however, within our hands, plant metabolomics is an HTP and informative "omics" approach to both sample generation and data generation, as well as raw data preprocessing, statistical analysis, and biological interpretation. Within this chapter, we aim to describe the great attention given to experimental design to ensure that the correct sample set and control are included and to, thereby, enable reliable statistical analysis of the data. For as comprehensive metabolite coverage as possible, we advocate the use of multiparallel approaches; thus, we describe a step-by-step standardized method for Nuclear magnetic resonance spectroscopy, as well as discussing with reference to standardized methodologies the techniques of gas chromatography-time of flight/mass spectrometry, and liquid chromatography-mass spectrometry.